Fault Detection in Industrial IoT Using Sensor Fusion Techniques

Abstract

 In recent years, the Industrial Internet of Things (IIoT) has revolutionized automation and production by integrating smart sensors and advanced analytics. However, ensuring system reliability and minimizing downtime remain major challenges. Fault detection is a crucial element of industrial operations, as undetected faults can lead to significant financial losses and safety hazards. This paper presents an analytical review of fault detection in IIoT systems using sensor fusion techniques, which combine data from multiple sensors to enhance detection accuracy. The study evaluates various fusion methodologies—such as Kalman filtering, Bayesian inference, and neural network-based fusion—highlighting their effectiveness in detecting anomalies in real time. The results show that multi-sensor integration improves the robustness of fault identification, reduces false alarms, and supports predictive maintenance in smart manufacturing environments. The paper concludes that sensor fusion plays a key role in advancing the reliability and resilience of industrial IoT infrastructures.